1. Technical Field of the Invention
The invention relates generally to communication systems; and, more particularly, it relates to decoding of encoded signals within such communication systems.
2. Description of Related Art
Data communication systems have been under continual development for many years. One such type of communication system that has been of significant interest lately is a communication system that employs turbo codes. Another type of communication system that has also received interest is a communication system that employs Low Density Parity Check (LDPC) code. A primary directive in these areas of development has been to try continually to lower the error floor within a communication system. The ideal goal has been to try to reach Shannon's limit in a communication channel. Shannon's limit may be viewed as being the data rate to be used in a communication channel, having a particular Signal to Noise Ratio (SNR), that achieves error free transmission through the communication channel. In other words, the Shannon limit is the theoretical bound for channel capacity for a given modulation and code rate.
LDPC code has been shown to provide for excellent decoding performance that can approach the Shannon limit in some cases. For example, some LDPC decoders have been shown to come within 0.3 dB from the theoretical Shannon limit. While this example was achieved using an irregular LDPC code of a length of one million, it nevertheless demonstrates the very promising application of LDPC codes within communication systems.
There is, however, at least one undesirable aspect of decoding processing of LDPC coded signals. The decoding of LDPC codes typically involves using iterative processing; this iterative processing inherently includes some loops. There are several approaches that may be used to perform decoding of LDPC codes. One approach involves a Belief Propagation (BP) decoding approach. The BP approach may be implemented either using a Sum Product (SP) approach or a Message Passing (MP) approach. However, given the fact that the manner in which these LDPC codes are decoded include loops, the possibility of oscillations may arise in the decoding within theses loops.
As described very accurately by Pearl, “when loops are present, the network is no longer singly connected and local propagation schemes will invariably run into trouble . . . If we ignore the existence of loops and permit the nodes to continue communicating with each other as if the network were singly connected, messages may circulate indefinitely around the loops and process may not converges to a stable equilibrium . . . Such oscillations do not normally occur in probabilistic networks. . . which tend to bring all messages to some stable equilibrium as time goes on. However, this asymptotic equilibrium is not coherent, in the sense that it does not represent the posterior probabilities of all nodes of the network.” J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988.
As can clearly be seen by the description provided above, a need exists in the art to try to deal with these undesirable oscillations that can sometimes occur when decoding LDPC coded signals within communication decoders.